Source candidates whose skills match what current academic literature actually demands, without switching between tools.
Pull skills directly from peer-reviewed papers before writing a single job requirement.
Move from academic research to candidate search inside one conversation without manual transfers.
Convert citation-heavy technical terms into high-signal Dice keyword filters automatically.
Pull recent papers on a technical domain and extract the skills most cited before drafting your job description.
Identify prolific Consensus authors in a niche field, then search Dice for practitioners with matching profiles.
Document publication expertise with Consensus, then run recurring Dice searches for roles where that expertise is valued.
Find Dice listings for roles matching literature-identified skills and filter by salary range to anchor compensation.
Use author affiliation data from Consensus to focus Dice sourcing around top research-producing institutions.
Define participant criteria from Consensus literature, then filter Dice for UX researchers or study coordinators.
Ask Neotask to query Consensus for recent papers in your target domain and extract the most frequently cited skills and methods.
Neotask maps the literature-derived terms into structured Dice keyword and filter parameters automatically.
Run the Dice search with your location, salary, and experience filters applied, and review the resulting candidate or job listing set.
| Capability | Consensus | Dice |
|---|---|---|
| Search academic papers | Yes | No |
| Extract cited skills and methods | Yes | No |
| Search tech candidates and jobs | No | Yes |
| Filter by location and salary | No | Yes |
| Cross-platform keyword mapping | Via Neotask | Via Neotask |
| Recurring search automation | Via Neotask | Via Neotask |
Hiring for research-intensive roles is different from standard tech recruiting. Job titles are vague, skills shift quickly, and the gap between academic expertise and industry demand is rarely obvious from a resume alone.
Consensus indexes over 200 million peer-reviewed papers and returns evidence-based answers with citations. Dice focuses on tech professionals and provides filterable job and candidate searches by skill, location, salary, and role type. Together, they give you a recruiting workflow that starts with what the science says matters and ends with candidates who actually have it.
Most recruiters start with a job title and work backward. That approach misses the specific methods, frameworks, and terminology that define real expertise in fast-moving fields like machine learning, computational biology, or academic UX research.
Starting from the literature flips this. When you search Consensus for papers on transformer fine-tuning or federated learning, you get the exact vocabulary researchers use. Those terms become your Dice search keywords, which means your candidate pool is filtered against what the field actually values - not what a recruiter assumed the field values.
You can use Neotask to run both searches in one conversation. Ask it to find top papers on a topic, extract the most common methods and skills mentioned, and immediately search Dice for candidates with those qualifications. You can filter by location, salary range, employment status, and seniority - all without switching tabs or copying data between tools.
For recurring needs, you can set up ongoing Dice searches tied to literature reviews that refresh as new papers are published. This is especially useful for fast-moving fields where required skills evolve within a hiring cycle.
This workflow is built for recruiting coordinators and researchers working in ML, data science, computational biology, clinical research, and academic UX. It also benefits researchers transitioning to industry who want to articulate their publication background in terms that match active job listings.
Start with a Consensus literature review before opening Dice - the exact terms researchers use in papers convert directly into high-precision candidate search keywords.
Use citation counts as a proxy for skill demand: skills appearing frequently across highly-cited papers signal what the research community values most.
Cross-reference Consensus author institution locations with Dice location filters to focus sourcing in regions where research talent is already concentrated.
Neotask connects to both platforms through your existing accounts. Once authorized, you can issue natural-language commands that pull data from Consensus and Dice in the same workflow. Neotask passes context between steps so you never manually transfer information between tools.
Yes. Neotask can help you define participant criteria using Consensus literature, then search Dice for candidates matching those qualifications, filtered by skills, location, experience level, and employment status.
Yes. Researchers moving into industry can use Consensus to document their publication expertise, then instruct Neotask to run ongoing Dice searches for roles where that expertise is valued.
Research-heavy hiring roles benefit most, including ML research, data science, computational biology, and academic UX. Any workflow where understanding the science behind a role matters will improve by grounding requirements in current literature first.
Yes. The workflow applies to recruiters sourcing candidates and to researchers searching for roles. Both directions use Consensus to define relevant skills and Dice to surface matching opportunities or people.
Stop guessing what skills matter. Let Consensus surface what the research says, and let Dice find the people who have it. Neotask connects both in one workflow.
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